Probabilistic Pareto plan generation for semiautomated multicriteria radiation therapy treatment planning.

  title={Probabilistic Pareto plan generation for semiautomated multicriteria radiation therapy treatment planning.},
  author={Tianfang Zhang and Rasmus Bokrantz and Jimmy Olsson},
  journal={Physics in medicine and biology},
OBJECTIVE We propose a semiautomatic pipeline for radiation therapy treatment planning, combining ideas from machine learning-automated planning and multicriteria optimization (MCO). APPROACH Using knowledge extracted from historically delivered plans, prediction models for spatial dose and dose statistics are trained and furthermore systematically modified to simulate changes in tradeoff priorities, creating a set of differently biased predictions. Based on the predictions, an MCO problem is… 
1 Citations

Figures and Tables from this paper

Predicting scenario doses for robust automated radiation therapy treatment planning
Purpose: We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking. Methods: The scenario dose


A New Linear Programming Approach to Radiation Therapy Treatment Planning Problems
A new model that has the potential to achieve most of the goals with respect to the quality of a treatment plan for IMRT is proposed, in contrast with established mixed-integer and nonlinear programming formulations, while retaining linearity of the optimization problem.
Knowledge-based tradeoff hyperplanes for head-and-neck treatment planning.
  • Jiahan Zhang, Y. Ge, +6 authors Q. Wu
  • Medicine
    International journal of radiation oncology, biology, physics
  • 2020
Plan averaging for multicriteria navigation of sliding window IMRT and VMAT.
The proposed method enables the navigation of deliverable Pareto optimal plans directly, i.e., interactive multicriteria exploration of Deliverable sliding window IMRT and VMAT plans, eliminating the need for a sequencing step after navigation and hence the dose degradation that is caused by such a sequencingStep.
Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans.
Quantitative analysis of patient anatomical features and their correlation with OAR dose sparing has identified a number of important factors that explain significant amount of interpatient DVH variations in OARs.
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
The MSE+DVH+ADV model performed the best in these categories, illustrating the importance of both human and learned domain knowledge, and the effects of the domain specific loss on the model performance.
Multi-criteria optimization and decision-making in radiotherapy
Pareto-optimal plans as ground truth for validation of a commercial system for knowledge-based DVH-prediction.
  • E. Cagni, A. Botti, Yibing Wang, M. Iori, S. Petit, B. Heijmen
  • Medicine, Computer Science
    Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics
  • 2018
Probabilistic dose prediction using mixture density networks for automated radiation therapy treatment planning
It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs in contrast to deterministic methods previously investigated in the literature.
A Novel Machine Learning Model for Dose Prediction in Prostate Volumetric Modulated Arc Therapy Using Output Initialization and Optimization Priorities
A novel machine learning model for predicting the dose distribution for a given patient with a given set of optimization priorities is presented, which may be used for quickly estimating the Pareto set of feasible dose objectives and indirectly improve final plan quality by allowing more time for plan refinement.